Tomato Detection - Dataset Ninja

Summary #

Tomato Detection is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 895 images with 4930 labeled objects belonging to 1 single class (tomato).

Images in the Tomato Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020.

Explore #

Tomato Detection dataset has 895 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Tomato Detection

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Have a look at 895 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
tomato➔ rectangle	895	4930	5.51	12.38%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-1 of 1

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
tomato rectangle	4930	2.44%	87.88%	0.04%	5px	1.2%	487px	97.4%	55px	12.18%	6px	1.2%	409px	96.5%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 4930 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 4930

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	tomato rectangle	tomato517.png	500 x 400	47px	9.4%	39px	9.75%	0.92%
2➔	tomato rectangle	tomato517.png	500 x 400	43px	8.6%	51px	12.75%	1.1%
3➔	tomato rectangle	tomato517.png	500 x 400	35px	7%	31px	7.75%	0.54%
4➔	tomato rectangle	tomato517.png	500 x 400	32px	6.4%	30px	7.5%	0.48%
5➔	tomato rectangle	tomato517.png	500 x 400	38px	7.6%	34px	8.5%	0.65%
6➔	tomato rectangle	tomato517.png	500 x 400	29px	5.8%	35px	8.75%	0.51%
7➔	tomato rectangle	tomato890.png	500 x 400	39px	7.8%	37px	9.25%	0.72%
8➔	tomato rectangle	tomato890.png	500 x 400	37px	7.4%	46px	11.5%	0.85%
9➔	tomato rectangle	tomato107.png	500 x 400	53px	10.6%	38px	9.5%	1.01%
10➔	tomato rectangle	tomato107.png	500 x 400	58px	11.6%	62px	15.5%	1.8%

License #

Tomato Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the Tomato Detection data, please cite the following reference:

@misc{make ml,
title={Tomato Dataset},
url={https://makeml.app/datasets/tomato},
journal={Make ML}}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-tomato-detection-dataset,
  title = { Visualization Tools for Tomato Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/tomato-detection } },
  url = { https://datasetninja.com/tomato-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-01 },
}

Download #

Dataset Tomato Detection can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Tomato Detection', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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Disclaimer #

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